Computational and systems biology

Theme leader: Professor Edmund Crampin, University of Melbourne

The CBNS uses mathematical and computational modelling to analyse high-throughput experimental data to understand and ultimately predict the interactions between nano-engineered materials and biological systems.

A major unmet challenge to advancing bio-nano technology is the ability to predict ab initio how a biological system will respond when exposed to a particular type of nanomaterial. This understanding would create a pathway for engineering design of nanomaterials with desired and predictable biological interactions. A significant hurdle to overcome is the development of standards and approaches which will allow data from different experimental investigations to be combined and analysed in order to identify underlying patterns in the data. Such patterns reveal the properties of nanomaterials which dictate their biological interactions. The CBNS are developing strategies for data collection and computational approaches for modelling of data generated in the Centre to better understand, and hence predict, how specific properties of nanoscale materials lead to specific biological responses.

CBNS Annual Reports provide details of the research of specific activities within the Computational and systems biology theme.


Schematic showing, from the top, (a) biological pathway (glycolytic pathway), (b) Bond Graph representation, and (c) physically-plausible mathematical model generated from the Bond Graph representation.
Gawthrop PJ, Cursons J, Crampin EJ. (2015) Proc. R. Soc. A 471: 20150642